In bit-plane coding, one seeks to try to reduce the coding amount by restricting the bit-planes coded to a fraction of the total amount of available bit-planes. Mostly, the bit-plane coding is performed on transform coefficients, i.e. coefficients of a transform of the actual data to be coded such as a spectral decomposition transform of a picture. Such a transform already “condenses” the overall signal energy into a smaller amount of samples, namely transform coefficients, and results in neighboring transform coefficients sharing similar statistics as far as the position of the most significant bit-plane among the available bit-planes is concerned, i.e. the most significant bit-plane having a non-zero bit in the respective transform coefficient. Accordingly, in the currently envisaged version of the upcoming JPEG XS, the transform coefficients representing a picture are coded in units of groups of transform coefficients with the datastream spending a syntax element per transform coefficient group which indicates the largest, i.e. most significant, bit-plane populated by the bits of the transform coefficients within that group, called GCLI, greatest coded line index. Alternative names are MSB Position or bitplane count. This GCLI value is coded in the datastream in a predictive manner such as using spatial prediction from neighboring transform coefficient groups. Such GCLI groups, in turn, are grouped into SIG groups and for each such SIG group of GCLI groups, a flag is spent in the datastream signaling the case where the prediction residual coded for the GCLI values is all zero for all the GCLI groups within an SIG group. If such a flag signals that all prediction residuals for GCLI are zero within an SIG group, no GCLI prediction residuals need to be transmitted and bitrate is saved.
However, there is still an ongoing wish to improve the coding efficiency of the just-outlined bit-plane concept in terms of, for instance, compression and/or coding complexity.